Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

As the demand for quality healthcare increases, healthcare systems worldwide are
grappling with time constraints and excessive workloads, which can compromise the quality …

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …

Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …

Recent advances in generative AI have brought incredible breakthroughs in several areas,
including medical imaging. These generative models have tremendous potential not only to …

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep
learning (DL) techniques, have enabled broad application of these methods in health care …

evolved separately and have never worked together well. Thus, it is not surprising that
radiologists often work with disjointed system integrations and clashing user interfaces …

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic,
pathological, and clinical findings is vital. Management of ILD also requires thorough follow …

Large annotated datasets are required for training deep learning models, but in medical
imaging data sharing is often complicated due to ethics, anonymization and data protection …

Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice.
Recent advances in the development of vision-language foundation models (FMs) give rise …